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refactor(memory): use MemorySummary node count for implicit memory metrics

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- Replace Statement-based implicit memory count (count/3) with actual
  MemorySummary node count filtered by DERIVED_FROM_STATEMENT relationship
- Add minimum threshold of 5 MemorySummary nodes before reporting data
- Add _build_empty_profile() to return structured empty profile when
  insufficient data exists, skipping unnecessary LLM calls
This commit is contained in:
lanceyq
2026-04-13 18:32:43 +08:00
parent 05ea372776
commit ef8c7093b5
2 changed files with 72 additions and 11 deletions
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22
api/app/services/user_memory_service.py
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@@ -1500,7 +1500,7 @@ async def analytics_memory_types(
2. 工作记忆 (WORKING_MEMORY) = 会话数量（通过 ConversationRepository.get_conversation_by_user_id 获取）
3. 短期记忆 (SHORT_TERM_MEMORY) = /short_term 接口返回的问答对数量
4. 显性记忆 (EXPLICIT_MEMORY) = 情景记忆 + 语义记忆（通过 MemoryBaseService.get_explicit_memory_count 获取）
5. 隐性记忆 (IMPLICIT_MEMORY) = Statement 节点数量的三分之一
5. 隐性记忆 (IMPLICIT_MEMORY) = MemorySummary 节点数量（需 >= 5 才显示，否则为 0）
6. 情绪记忆 (EMOTIONAL_MEMORY) = 情绪标签统计总数（通过 MemoryBaseService.get_emotional_memory_count 获取）
7. 情景记忆 (EPISODIC_MEMORY) = memory_summary（通过 MemoryBaseService.get_episodic_memory_count 获取）
8. 遗忘记忆 (FORGET_MEMORY) = 激活值低于阈值的节点数（通过 MemoryBaseService.get_forget_memory_count 获取）
@@ -1557,23 +1557,23 @@ async def analytics_memory_types(
logger.warning(f"获取会话数量失败，工作记忆数量设为0: {str(e)}")
work_count = 0
# 获取隐性记忆数量（基于 Statement 节点数量的三分之一）
# 获取隐性记忆数量（基于有关联关系的 MemorySummary 节点数量，需 >= 5 才计入）
implicit_count = 0
if end_user_id:
try:
# 查询 Statement 节点数量
# 只统计有 DERIVED_FROM_STATEMENT 关系的 MemorySummary 节点，排除孤立节点
query = """
MATCH (n:Statement)
MATCH (n:MemorySummary)-[:DERIVED_FROM_STATEMENT]->(:Statement)
WHERE n.end_user_id = $end_user_id
RETURN count(n) as count
RETURN count(DISTINCT n) as count
"""
result = await _neo4j_connector.execute_query(query, end_user_id=end_user_id)
statement_count = result[0]["count"] if result and len(result) > 0 else 0
# 取三分之一作为隐性记忆数量
implicit_count = round(statement_count / 3)
logger.debug(f"隐性记忆数量（Statement数量的1/3）: {implicit_count} (Statement总数={statement_count}, end_user_id={end_user_id})")
memory_summary_count = result[0]["count"] if result and len(result) > 0 else 0
# 仅当 MemorySummary 节点数量 >= 5 时才显示数量，否则为 0
implicit_count = memory_summary_count if memory_summary_count >= 5 else 0
logger.debug(f"隐性记忆数量（有效MemorySummary节点数）: {implicit_count} (有效MemorySummary总数={memory_summary_count}, end_user_id={end_user_id})")
except Exception as e:
logger.warning(f"获取Statement数量失败，隐性记忆数量设为0: {str(e)}")
logger.warning(f"获取MemorySummary数量失败，隐性记忆数量设为0: {str(e)}")
implicit_count = 0
# 原有的基于行为习惯的统计方式（已注释）
@@ -1639,7 +1639,7 @@ async def analytics_memory_types(
"WORKING_MEMORY": work_count, # 工作记忆（基于会话数量）
"SHORT_TERM_MEMORY": short_term_count, # 短期记忆（基于问答对数量）
"EXPLICIT_MEMORY": explicit_count, # 显性记忆（情景记忆 + 语义记忆）
"IMPLICIT_MEMORY": implicit_count, # 隐性记忆（Statement数量的1/3）
"IMPLICIT_MEMORY": implicit_count, # 隐性记忆（MemorySummary节点数，需>=5）
"EMOTIONAL_MEMORY": emotion_count, # 情绪记忆（使用情绪标签统计）
"EPISODIC_MEMORY": episodic_count, # 情景记忆
"FORGET_MEMORY": forget_count # 遗忘记忆（激活值低于阈值）